TensorFlowOnSpark

By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed
deep learning on a cluster of GPU and CPU servers.

It enables both distributed TensorFlow training and
inferencing on Spark clusters, with a goal to minimize the amount
of code changes required to run existing TensorFlow programs on a
shared grid. Its Spark-compatible API helps manage the TensorFlow
cluster with the following steps:

Startup - launches the Tensorflow main function on the executors, along with listeners for data/control messages.

Usage

To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our Conversion Guide to describe the required changes. Additionally, our wiki site has pointers to some presentations which provide an overview of the platform.